Create ai_risk_assessment

blockchain_integration/pi_network/ai_risk_assessment

@@ -0,0 +1,35 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+import joblib
+
+class RiskAssessmentModel:
+    def __init__(self):
+        self.model = RandomForestClassifier(n_estimators=100, random_state=42)
+
+    def train(self, data_path):
+        # Load dataset
+        data = pd.read_csv(data_path)
+        X = data.drop('risk_label', axis=1)
+        y = data['risk_label']
+
+        # Split the dataset
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+        # Train the model
+        self.model.fit(X_train, y_train)
+
+        # Evaluate the model
+        predictions = self.model.predict(X_test)
+        accuracy = accuracy_score(y_test, predictions)
+        print(f'Model accuracy: {accuracy:.2f}')
+
+    def save_model(self, model_path):
+        joblib.dump(self.model, model_path)
+
+    def load_model(self, model_path):
+        self.model = joblib.load(model_path)
+
+    def predict(self, input_data):
+        return self.model.predict([input_data])